(IV) 化合物作为潜在的药物:一个定量结构-活性关系研究研究.
Jurica Novak1,2, Alena R Zykova3, Vladimir A Potemkin1,2,3
1Department of Biotechnology, University of Rijeka, Rijeka, Croatia.
机器学习模型预测 (IV) 复合体用于药物发现. 特定的复合物显示为对抗SARS-CoV的抑制剂,有助于抗病毒药物开发的承诺.
科学领域:
- 计算化学是一种计算化学.
- 药品化学 药品化学 是一个
- 药物发现 药物发现
背景情况:
- 机器学习 (ML) 和增加的计算能力正在彻底改变药物设计和重新利用.
- 预测模型可以加速新型治疗剂的识别.
研究的目的:
- 使用ML. 预测新的 (IV) 复合物的生物活性.
- 开发和验证定量结构-活动关系 (QSAR) 模型,用于预测针对SARS-CoV的活动.
主要方法:
- 利用了在chemosophia.com.com上提供的ML预测模型.
- 开发并验证了基于BiS算法的两个新的QSAR模型.
- 使用十倍交叉验证来评估模型的预测能力 (交叉R2 0.8630.903).
主要成果:
- 预计18个 (IV) 复合物的38个不同活动.
- 活动跨越了抗氧化,抗菌,抗病毒,抗炎,抗心律失常和抗疟疾的潜力.
- 确定了1,3和13复合体作为SARS-CoVRNA依赖RNA聚合酶的潜在抑制剂.
结论:
- 开发的QSAR模型显示出高预测准确度.
- (IV) 复合体1,3和13是SARS-CoV抑制的有希望的候选物.
- 基于机器学习的预测有助于高效的药物发现和重定向努力.
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